2025/02/28 by Engin Zeydan, Zeydan, Engin, Cristian J. Vaca-Rubio +10 · 1 voice
Computer Science · Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Privacy-Preserving Technologies in Data #Traffic Prediction and Management Techniques #cs.AI #cs.LG #cs.NI
paper · pdf · doi:10.48550/arxiv.2503.00154
openalex publication_date 2025/02/28 · arxiv published 2025/02/28 · arxiv updated 2025/02/28 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28
Non-Terrestrial Networks (NTNs) are becoming a critical component of modern communication infrastructures, especially with the advent of Low Earth Orbit (LEO) satellite systems. Traditional centralized learning approaches face major challenges in such networks due to high latency, intermittent connectivity and limited bandwidth. Federated Learning (FL) is a promising alternative as it enables decentralized training while maintaining data privacy. However, existing FL models, such as Federated Learning with Multi-Layer Perceptrons (Fed-MLP), can struggle with high computational complexity and poor adaptability to dynamic NTN environments. This paper provides a detailed analysis for Federated Learning with Kolmogorov-Arnold Networks (Fed-KAN), its implementation and performance improvements over traditional FL models in NTN environments for traffic forecasting. The proposed Fed-KAN is a novel approach that utilises the functional approximation capabilities of KANs in a FL framework. We evaluate Fed-KAN compared to Fed-MLP on a traffic dataset of real satellite operator and show a significant reduction in training and test loss. Our results show that Fed-KAN can achieve a 77.39% reduction in average test loss compared to Fed-MLP, highlighting its improved performance and better generalization ability. At the end of the paper, we also discuss some potential applications of Fed-KAN within O-RAN and Fed-KAN usage for split functionalities in NTN architecture.